modeling terrestrial and arboreal mammals at GPNP
Gene Estrada
2024-05-21
I. Camera Trap Data
SPECIES RICHNESS
Entire Study Area
Excluding arboreal mammals, dogs, and taxa not identified to species level (Muntiacus spp., Tragulus spp. and Unid civets). All camera trap locations: 36 total species.
## SpeciesRichness
## 1 36Compared to only CT locations that have LiDAR data: 34 total species
## SpeciesRichness
## 1 34By forest type
Excluding arboreal mammals
## # A tibble: 7 × 4
## habitat n.scanned n.all diff
## <ord> <int> <int> <int>
## 1 Montane 22 29 7
## 2 Upland Granite 22 24 2
## 3 Lowland Granite 20 22 2
## 4 Lowland Sandstone 24 27 3
## 5 Alluvial Bench 27 31 4
## 6 Freshwater Swamp 28 31 3
## 7 Peat Swamp 20 23 3By partition
Excluding unid animals
## # A tibble: 11 × 4
## partition n.scanned n.all diff
## <ord> <int> <int> <int>
## 1 MO2 22 26 4
## 2 UG2 22 23 1
## 3 UG1 14 24 10
## 4 LG2 20 21 1
## 5 LG1 18 20 2
## 6 LS2 19 25 6
## 7 LS1 23 26 3
## 8 AB2 22 27 5
## 9 AB1 21 24 3
## 10 FS1 28 31 3
## 11 PS1 20 23 3Again, but for terrestrial mammals only
By camera trap location
Excluding arboreal mammals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 6.000 10.000 9.659 13.000 19.000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 10.00 13.00 12.21 15.75 19.00SHANNON DIVERSITY
By camera trap location
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.339 1.634 1.589 1.988 2.484
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3046 1.4367 1.8424 1.7670 2.1257 2.4841By partition
Comparing Shannon diversity aggregated at partition for all ct locations to only locations with LiDAR data
## partition shannon_all shannon_scan diff
## 1 PS1 1.668766 1.760385 -0.09161834
## 2 FS1 2.189094 2.100386 0.08870785
## 3 AB1 2.459775 2.435128 0.02464764
## 4 AB2 2.251804 2.282600 -0.03079550
## 5 LS1 2.417306 2.332208 0.08509861
## 6 LS2 2.245537 2.216715 0.02882264
## 7 LG1 2.130200 2.106380 0.02382062
## 8 LG2 2.301889 2.269269 0.03262019
## 9 UG1 1.996149 1.872830 0.12331926
## 10 UG2 2.399767 2.330706 0.06906144
## 11 MO1 2.373866 2.429092 -0.05522585
## 12 MO2 2.340188 2.239893 0.10029504Creating Shannon diversity metrics for terrestrial mammals only, for all partitions and partitions with montane aggregate
By forest type
## habitat shannon_all shannon_scan diff
## 1 Peat Swamp 1.668766 1.760385 -0.09161834
## 2 Freshwater Swamp 2.189094 2.100386 0.08870785
## 3 Alluvial Bench 2.380981 2.411738 -0.03075691
## 4 Lowland Sandstone 2.366832 2.337795 0.02903769
## 5 Lowland Granite 2.247289 2.235305 0.01198368
## 6 Upland Granite 2.265847 2.173927 0.09191952
## 7 Montane 2.425421 2.385883 0.03953851SPECIES EVENNESS
By camera trap location
Pielou’s diversity measure of species evenness
By partition
Pielou’s diversity measure of species evenness
adding diversity metrics to forest structure metrics df
FUNCTIONAL DIVERSITY METRICS
By camera trap location
## Species x species distance matrix was not Euclidean. 'sqrt' correction was applied.
## FEVe: Could not be calculated for communities with <3 functionally singular species.
## FDis: Equals 0 in communities with only one functionally singular species.
## FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
## FRic: Dimensionality reduction was required. The last 32 PCoA axes (out of 34 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.5079213
## FDiv: Could not be calculated for communities with <3 functionally singular species.By partition
## Species x species distance matrix was not Euclidean. 'sqrt' correction was applied.
## FRic: Dimensionality reduction was required. The last 16 PCoA axes (out of 34 in total) were removed.
## FRic: Quality of the reduced-space representation (based on corrected distance matrix) = 0.9930462COMMUNITY METRICS VISUALIZATION
Community metrics by CT location/forest type
Community metrics by elevation
DISTANCE x DISSIMILARITY
Calculating pairwise distance for scanned locations and plotting
values by pairwise similarity, with blue points showing pairs in the
same forest type and red points pairs in different partitions. I include
community metrics here as well, but it might be worthwhile to see what
the linear models look like for these for the full set of camera traps,
since we know that the community metrics for only the scanned sites
differs from the values for all CT sites, especially in the higher
elevation partitions.
Comparison table showing intercepts and slopes of the linear models plotted above. 11/15 structure metrics show a lower intercept for the ‘same partition’ group compared to the ‘different partition’ group, while 9/15 show lower slope values for the ‘same partition’ group. ‘inst.s.lt.d’ = intercept ‘same’ less than ‘different’. ‘slo.s.lt.d’ = slope ‘same’ less than ‘different’
## metric int.same int.diff slope.same
## (Intercept) max.height 3.789191e+00 3.760276e+00 1.054268e-03
## (Intercept)1 sd.r 1.073502e+00 9.571490e-01 -2.550752e-04
## (Intercept)2 CRR.rho 1.916169e-01 1.831533e-01 -1.282471e-05
## (Intercept)3 pts.below.2m 2.950892e+06 3.958830e+06 9.446607e+02
## (Intercept)4 stand.dens 7.849300e+01 8.429609e+01 -3.981789e-03
## (Intercept)5 basal.area 4.946700e+00 5.545563e+00 -8.184574e-05
## (Intercept)6 stem.vol 4.995756e+01 5.176276e+01 -4.381117e-03
## (Intercept)7 mean.tree.h 3.546187e+00 3.995532e+00 1.699292e-06
## (Intercept)8 mean.dbh 3.565030e+00 2.987799e+00 -6.476197e-04
## (Intercept)9 zentropy 3.026188e-02 3.533461e-02 1.709801e-05
## (Intercept)10 lad.max 1.564091e-01 1.717889e-01 4.660414e-05
## (Intercept)11 rumple 4.862681e+00 6.331417e+00 -9.590519e-04
## (Intercept)12 vFRcanopy 6.916247e+00 8.463821e+00 1.783825e-03
## (Intercept)13 vzrumple 2.486047e-01 2.550307e-01 1.569047e-04
## (Intercept)14 ClosedGapSpace 3.462682e+00 4.190533e+00 -2.296485e-04
## (Intercept)15 n.all 5.241039e+00 4.859741e+00 -1.953154e-03
## (Intercept)16 shannon_ct 4.579751e-01 4.186420e-01 -8.322650e-05
## (Intercept)17 div_even 1.336741e-01 1.286745e-01 -2.708887e-06
## slope.diff int.s.lt.d slo.s.lt.d
## (Intercept) 1.461212e-03 FALSE TRUE
## (Intercept)1 2.395128e-05 FALSE TRUE
## (Intercept)2 1.529234e-06 FALSE TRUE
## (Intercept)3 -1.123295e+02 TRUE FALSE
## (Intercept)4 -2.403966e-03 TRUE TRUE
## (Intercept)5 -2.776228e-04 TRUE FALSE
## (Intercept)6 -1.145231e-03 TRUE TRUE
## (Intercept)7 2.130025e-04 TRUE TRUE
## (Intercept)8 -1.804748e-05 FALSE TRUE
## (Intercept)9 2.466311e-06 TRUE FALSE
## (Intercept)10 1.196261e-05 TRUE FALSE
## (Intercept)11 -5.764033e-04 TRUE TRUE
## (Intercept)12 5.401880e-04 TRUE FALSE
## (Intercept)13 8.413938e-05 TRUE FALSE
## (Intercept)14 -9.417598e-05 TRUE TRUE
## (Intercept)15 8.086911e-06 FALSE TRUE
## (Intercept)16 4.321748e-05 FALSE TRUE
## (Intercept)17 7.772363e-06 FALSE TRUEagain for the 170 ha grid for comparison. 8/15 metrics show an intercept lower for within-cell pairs compared to between cells
SPATIAL AUTOCORRELATION
First I want to just visualize spatial patterns of structure
variables and community metrics across the study site, by camera trap
location.
Mapping the community metrics by all CT locations (the figure above
only plots scanned locations)
Global Moran’s I
A formal test of spatial autocorrelation across the entire study area. I may only be able to meaningfully test for spatial autocorrelation at this scale, and not at the grid or partition scales, as the typical low end of spatial units required for reliable statistical inference is 30.
Five structure metrics show a significant level of spatial autocorrelation, including max height, vertical rumple, vegetation volume, leaf area density, and mean tree height.
Looking at the spatial patterns for these metrics in the plots above, max height, vrumple, lad, and mean height seem to show a roughly similar pattern, with low values in the swamp forests and montane, and highest values in the lowland forests in the center of the trail system - exactly what we see in the boxplots of these values plotted by forest type. Vegetation volume (vFRcanopy) shows a different spatial pattern. And rumple, along with closedgapspace show an outlier in the center of the trail system - location BC 12.4 (not sure what’s going on with that one)
## # A tibble: 21 × 7
## Metric Moran_I Expected_I Variance_I Z_score P_value Significant
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 FDiver 0.416 -0.0179 0.00628 5.48 2.16e-8 TRUE
## 2 max.height 0.417 -0.0175 0.00642 5.42 3.01e-8 TRUE
## 3 vzrumple 0.299 -0.0175 0.00634 3.98 3.43e-5 TRUE
## 4 shannon_ct 0.293 -0.0175 0.00631 3.91 4.66e-5 TRUE
## 5 vFRcanopy 0.230 -0.0175 0.00623 3.13 8.64e-4 TRUE
## 6 lad.max 0.156 -0.0175 0.00640 2.17 1.51e-2 TRUE
## 7 mean.tree.h 0.146 -0.0175 0.00629 2.06 1.98e-2 TRUE
## 8 n.all 0.121 -0.0175 0.00640 1.73 4.20e-2 TRUE
## 9 div_even 0.116 -0.0175 0.00640 1.67 4.76e-2 TRUE
## 10 sd.r 0.0916 -0.0175 0.00643 1.36 8.67e-2 FALSE
## 11 pts.below.2m 0.0885 -0.0175 0.00629 1.34 9.06e-2 FALSE
## 12 FRich 0.0825 -0.0179 0.00629 1.27 1.03e-1 FALSE
## 13 stand.dens 0.0804 -0.0175 0.00645 1.22 1.11e-1 FALSE
## 14 stem.vol 0.0130 -0.0175 0.00623 0.387 3.49e-1 FALSE
## 15 rumple -0.00832 -0.0175 0.00470 0.135 4.46e-1 FALSE
## 16 CRR.rho -0.0163 -0.0175 0.00641 0.0151 4.94e-1 FALSE
## 17 ClosedGapSpace -0.0183 -0.0175 0.00549 -0.00964 5.04e-1 FALSE
## 18 basal.area -0.0286 -0.0175 0.00624 -0.140 5.56e-1 FALSE
## 19 zentropy -0.0543 -0.0175 0.00625 -0.465 6.79e-1 FALSE
## 20 FEven -0.0883 -0.0179 0.00639 -0.880 8.11e-1 FALSE
## 21 mean.dbh -0.105 -0.0175 0.00639 -1.09 8.63e-1 FALSEExtracting Moran’s eigenvector values to use in glm to control for
spatial autocorrelation
CREATING SURVEY GRIDS
Run grid size functions
Again for the grid that I created in ArcGIS - 650 ha 2X2
MODELING - Terrestrial mammals (CT data)
Descriptions of forest structure metrics used in models
I used {FORTLS} to extract the following metrics:
max.height - maximum height of trees in the stand, in m
mean.tree.h - mean tree height in the stand, in m.
CRR.rho - canopy relief ratio, using rho for horizontal distance. A measure of canopy variation, with lower scores indicating lower local variation in canopy surface, i.e., more uniform age canopy/ less canopy surface complexity.
sd.r - standard deviation of tree heights in stand in m. I’ve seen this value used as an approximation for vertical stratification.
mean.dbh - mean diameter at breast height of trees in stand, in cm (measured at height of 1.3m)
basal.area - basal area of trees in the stand (m2/ha)
stand.dens - density of trees in the stand (trees/ha)
stem.vol - volume of trees in the stand (m3/ha)
pts.below.2m - shows the number of points in the point cloud that are below 2m.
I used {lidr} and {lidRmetrics} to extract the following metrics:
zentropy - normalized Shannon diversity index of z (height) values. Describes vertical complexity.
lad.max - Leaf Area Density maximum value for 1m vertical bins. Describes maximum foliage cover of the point cloud.
rumple - Rumple index (rugosity), a ratio of the canopy surface area to it’s projected ground area.
vn - number of 1m voxels created for the following volumetric metrics.
vFRcanopy - ratio of filled to empty voxels, only counting cells within and below canopy, ignoring above.
vzrumple - vertical Rumple index.
ClosedGapSpace - volume of voxels that are classified as gaps underneath the canopy
Check correlation between community metrics and predictors.
PREDICTORS: Basal area is highly correlated (>0.8) with stem volume and stand density.
COMMUNITY METS: By camera trap location - species richness &
shannon, and FRich and shannon are moderately correlated (both 0.69),
FRich and richness are highly correlated (0.82)
Models by camera trap location
Fitting global model then using {dredge} to find models of best fit. Restricting models to five predictors to avoid overfitting. Excluding two highly correlated pairs in dredge models: basal area & stand density, and basal area & stem volume.
Species richness model
averaged model output - averaging all models
Shannon diversity model
averaged model output - averaging all models
Species evenness model
averaged model output - averaging all models
Functional richness model
Top model summary output
averaged model output - averaging all models
Functional evenness model
Top model summary output
averaged model output - averaging all models
Functional divergence model
Top model summary output
averaged model output - averaging all models
Comparing coefficient plot from averaged models
Plotting variables from models that are at least closely reliable predictors (50% CI’s don’t overlap zero).
MODELS BY GRID
75 ha grid
The 75 ha grid creates 17 groups, with most groups (grid cells) containing 2 - 4 scanned locations each.
Correlation of community metrics. Richness is highly correlated with
FRich and FDiver, and shannon is highly correlated with evenness. So
probably should only keep shannon and the functional metrics.
Correlation of structure metrics - basal area and stand dens, basal
area and stem vol, %<2m and sd.r, max h and mean h, max h and
vrumple, and mean h and vrumple
Species richness model
Fitting global model then using {dredge} to find models of best fit.
Top model summary output. Restricting models to two predictors to avoid overfitting
averaged model output - averaging all models
Species diversity model
averaged model output - averaging all models
Species eveness model
averaged model output - averaging all models
Functional richness model
averaged model output - averaging all models
Functional evenness model
averaged model output - averaging all models
Functional divergence model
averaged model output - averaging all models
Comparing coefficient plots from averaged models
Only for shannon diveristy and functional models, based on high
degree of correlation with the other community metrics
170 ha grid
The 170 ha grid creates 10 groups (grid cells)
Correlation of community metrics. Richness is highly correlated with
FRich (0.87) and shannon is highly correlated with evenness (0.93). So
probably should only keep shannon and the functional metrics.
Correlation of structure metrics - basal area and stand dens, basal
area and mean dbh, max h and mean h, max h and vrumple, rumple and lad,
Species richness model
averaged model output - averaging all models
## null device
## 1Species diversity model
averaged model output - averaging all models
Species eveness model
averaged model output - averaging all models
Functional richness model
The beta coefficients are very large here, not sure why this is so much different from the rest of the models. Maybe an odd convergence issue?
averaged model output - averaging all models
Functional evenness model
averaged model output - averaging all models
Functional divergence model
averaged model output - averaging all models
Comparing coefficient plots from averaged models
Showing only richness, shannon, FEven, and FDiver models, since FRich
seemingly had nonsense results
MODELS BY PARTITION
Check correlation between outcome variables and predictors.
Correlation of community metrics. Richness is highly correlated with
FRich and shannon is highly correlated with evenness. As above, FRich
shows a large outlier in one of the group averages.
Basal area is highly correlated (>0.8) with stand density, max height with mean tree h and vzrumple, and vzrumple with mean tree height and rumple.
Species richness model
Fitting global model then using {dredge} to find models of best fit.
Top model summary output. Restricting models to two predictors to avoid overfitting
averaged model output - averaging all models
## null device
## 1Species diversity model
Top model summary output.
averaged model output - averaging all models
Species evenness model
Top model summary output.
averaged model output - averaging all models
Functional richness model
The model output here seems odd. After looking at the fric values closer, there seems to be an outlier here which may be throwing off the analysis. Not sure what to do here other than not include it in the final analysis, which may be warranted based on the high correlation between the richness and fric values (0.81)
averaged model output - averaging all models
Functional evenness model
Top model summary output.
averaged model output - averaging all models
Functional divergence model
Top model summary output.
averaged model output - averaging all models
Comparing coefficient plot from averaged models
Since fric is highly correlated with richness (0.81) it makes sense to ignore this model here. Of the other models that have reliable predictors, the shannon and evenness values are also highly correlated (0.89) and both show vFRcanopy (vegetated volume) as the sole reliable predictor, so I also omit the evenness model here.
Comparing avg model multiplots for CT and partition scales
Comparing effects of scale by outcome variable
Species richness
Species diversity
Again for Shannon diversity, but reliable predictors only
Species evenness
Functional richness
Functional evenness
Functional divergence
ORDINATION ANALYSIS
Using PCA to see how scan locations group together and to identify
which forest structure metrics best differentiate between forest types
and partitions.
Which structure metrics best differentiate between locations?
Table of variable PCA contribution values
## # A tibble: 15 × 2
## variable total_contrib
## <chr> <dbl>
## 1 basal.area 89.6
## 2 mean.tree.h 88.4
## 3 max.height 79.5
## 4 vzrumple 68.6
## 5 stem.vol 68.3
## 6 rumple 65.1
## 7 stand.dens 60.8
## 8 ClosedGapSpace 56.5
## 9 pts.below.2m 46.9
## 10 mean.dbh 41.9
## 11 sd.r 41.3
## 12 lad.max 31.9
## 13 zentropy 31.1
## 14 vFRcanopy 27.2
## 15 CRR.rho 2.79BIVARIATE PLOTS
terrestrial mammals (ct data) by ct location
Structure metrics X species richness
Structure metrics X Shannon diversity
Structure metrics X Species Evenness
Structure metrics X Functional Richness
Structure metrics X Functional Evenness
Structure metrics X Functional Divergence
terrestrial mammals (ct data) by partition
Structure metrics X species richness
Structure metrics X Shannon diversity
Structure metrics X Species Evenness
| Richness | Shannon Diversity | Evenness | Func. Richness | Func. Evenness | Func. Divergence | Richness | Shannon Diversity | Evenness | Func. Richness | Func. Evenness | Func. Divergence | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| max.height | -0.2822665 | -0.3473963 | -0.1104631 | -0.3501106 | -0.0092927 | -0.0180895 | -0.0606541 | -0.0892353 | -0.0616092 | -0.0299001 | -0.6616431 | 0.2567726 |
| sd.r | 0.1366387 | 0.1075038 | -0.0524271 | 0.0512050 | -0.2323421 | -0.0934397 | 0.4907328 | 0.2844402 | 0.1164183 | 0.3034671 | -0.3208231 | 0.4682592 |
| CRR.rho | -0.1384227 | -0.1656814 | -0.1351897 | 0.0590025 | 0.0455121 | -0.0259802 | -0.2350246 | -0.2878944 | -0.1892790 | -0.0155821 | -0.2928088 | 0.3342937 |
| pts.below.2m | -0.0373671 | -0.1858660 | -0.2586856 | -0.1331214 | -0.1344263 | -0.1652155 | 0.7039527 | 0.1522458 | -0.0934773 | 0.5010931 | -0.2090541 | -0.0752225 |
| stand.dens | 0.3639998 | 0.3503880 | 0.0094444 | 0.3978090 | -0.2071896 | 0.0289664 | 0.2540969 | 0.3944726 | 0.3157581 | 0.2429591 | 0.3659963 | 0.1044852 |
| basal.area | 0.2649925 | 0.3049558 | 0.0734725 | 0.2210968 | -0.0917111 | -0.0339108 | 0.0731401 | 0.4398316 | 0.4334561 | 0.2395054 | 0.1886525 | 0.0451641 |
| stem.vol | 0.2552954 | 0.3036081 | 0.1124067 | 0.1919080 | -0.0063824 | 0.1078632 | -0.1502708 | 0.4492890 | 0.5219881 | 0.0616719 | 0.0387210 | 0.5341543 |
| mean.tree.h | -0.0877811 | -0.1066189 | -0.0080111 | -0.2599512 | -0.0765966 | 0.0291181 | -0.1133321 | -0.0552613 | -0.0043525 | -0.0587486 | -0.6701856 | 0.2090019 |
| mean.dbh | -0.0111331 | 0.0211614 | 0.0084297 | -0.1317456 | 0.0534071 | -0.0689409 | -0.2418334 | 0.1612925 | 0.2746417 | 0.1527113 | -0.3235092 | -0.1038412 |
| zentropy | 0.1625037 | 0.2713834 | 0.1940172 | 0.1645552 | -0.0766785 | 0.2367055 | -0.1977574 | 0.1336686 | 0.2014013 | -0.2096010 | 0.5166602 | 0.0718520 |
| lad.max | 0.0900443 | 0.1415487 | 0.1229312 | 0.1186556 | -0.2089666 | 0.0281173 | 0.0236891 | 0.3488180 | 0.3407154 | 0.0597146 | -0.3630760 | 0.4903437 |
| rumple | 0.1010440 | 0.0289683 | -0.0072435 | -0.0817517 | 0.0184174 | 0.0902568 | -0.0510142 | 0.2803597 | 0.2732869 | -0.2904670 | -0.2530193 | 0.0537974 |
| vFRcanopy | 0.2868723 | 0.2197312 | -0.0868493 | 0.2935773 | 0.1033008 | 0.0374483 | 0.0678703 | 0.3658665 | 0.3394809 | 0.1350511 | 0.6458722 | -0.0900869 |
| vzrumple | -0.0764892 | -0.2248555 | -0.2254738 | -0.1783032 | 0.0698351 | -0.0816645 | -0.1178366 | -0.0060737 | 0.0383544 | -0.0418600 | -0.6232796 | 0.2198470 |
| ClosedGapSpace | 0.0262216 | 0.0010171 | 0.0707572 | -0.1061820 | -0.0547225 | 0.1677327 | -0.0621519 | 0.1262294 | 0.1124200 | -0.4682253 | -0.1449529 | -0.1121964 |
Spatial heterogeneity in the grid systems compared to partitions
How do forest types and partitions map on to the grid systems created above?
In the 75 ha grid system, cells contain between 1 and 6 partitions, with an average of 2.8 paritions per cell. In the 170 ha grid system this range is 2 - 7 with an average of 3.2.
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## 1 3 3 2 1 2 2 5 6 4 4 4 2 5 4 2 2 2 1 3 3 2 2 2## [1] 2.791667##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 2 4 2 2 3 7 4 4 2 4 2 2## [1] 3.166667What proportion of partitions are within each cell of the two grid
systems? I still need to choose a color scheme for the partitions,
something that matches the forest type colors.
How does variation in structure metrics compare between scales? The table shows the average adjusted coefficient of variation (which controls for sample size) of structure metrics across cells and partitions, comparing across the different scales. If grid systems are capturing more ecological variation in these metrics compared to forest type partitions, then we would expect the 170 ha grid average values to be larger than those at the partition scale. This is indeed what we see, with most (12/15) structure metrics and half (3/6) community metrics showing less variation at the partition scale than the comparable 170 ha grid scale.
## # A tibble: 21 × 12
## metric stat `75 ha` `170 ha` `650 ha` partition `study area` `pt<170ha`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 max.height _adj… 0.0822 0.0969 0.118 0.0913 0.128 TRUE
## 2 sd.r _adj… 0.187 0.180 0.184 0.182 0.208 FALSE
## 3 CRR.rho _adj… 0.328 0.340 0.357 0.336 0.352 TRUE
## 4 pts.below.… _adj… 0.310 0.367 0.373 0.379 0.401 FALSE
## 5 stand.dens _adj… 0.407 0.426 0.414 0.408 0.407 TRUE
## 6 basal.area _adj… 0.562 0.644 0.668 0.591 0.648 TRUE
## 7 stem.vol _adj… 0.694 0.745 0.733 0.737 0.723 TRUE
## 8 mean.tree.h _adj… 0.166 0.151 0.198 0.177 0.202 FALSE
## 9 mean.dbh _adj… 0.159 0.148 0.144 0.144 0.139 TRUE
## 10 zentropy _adj… 0.0599 0.0680 0.0678 0.0609 0.0685 TRUE
## 11 lad.max _adj… 0.164 0.205 0.233 0.197 0.231 TRUE
## 12 rumple _adj… 0.275 0.308 0.387 0.296 0.440 TRUE
## 13 vFRcanopy _adj… 0.198 0.234 0.238 0.180 0.239 TRUE
## 14 vzrumple _adj… 0.0770 0.0912 0.106 0.0758 0.112 TRUE
## 15 ClosedGapS… _adj… 0.380 0.425 0.490 0.395 0.507 TRUE
## 16 n.all _adj… 0.346 0.313 0.358 0.316 0.350 FALSE
## 17 shannon_ct _adj… 0.193 0.206 0.243 0.190 0.255 TRUE
## 18 div_even _adj… 0.133 0.144 0.164 0.146 0.173 FALSE
## 19 FRich _adj… 0.273 0.248 0.291 0.234 0.290 TRUE
## 20 FEven _adj… 0.148 0.151 0.158 0.157 0.161 FALSE
## 21 FDiver _adj… 0.0852 0.106 0.0978 0.0776 0.123 TRUE
## # ℹ 4 more variables: `75<170` <lgl>, `170<650` <lgl>, `650<SA` <lgl>,
## # `75<SA` <lgl>Figure showing how average adjusted coefficient of variation (which
controls for number of camera trap locations in each cell) increases as
grid size increases across the 15 forest structure metrics. Some metrics
reach an asymptote at 170 or 650 ha scale, and don’t show more variation
even at the study area scale. However, all but one of the metrics (mean
dbh; highlighted) increase in variation from the 75 ha to the study area
scale.
Spatial scales in ecology lit
Using the publications (n = 192) reviewed in Stein et al. (2014) to explore the distribution of spatial scales in ecological research. Taxa include plants (pla), herps (her), invertebrates (inv), birds (bir), and mammals (mam).
